Important

tensorflow-das 是面向 DAS 架构的 tensorflow 下游适配发行版， 基于 ROCm/tensorflow-upstream 的 1a34d8ff22cc6b53e2176106c39f3726d7f76905 基线构建并集成 DAS 支持。

本项目不是 tensorflow 官方发行版，而是基于 ROCm/tensorflow-upstream 二次开发；ROCm tensorflow 源自 tensorflow/tensorflow 二次开发。

• 目标架构： tensorflow 2.13.1
• 上游分支： r2.13-rocm-enhanced
• 上游版本： 1a34d8ff22cc6b53e2176106c39f3726d7f76905
• 上游基线： https://github.com/ROCm/tensorflow-upstream/commit/1a34d8ff22cc6b53e2176106c39f3726d7f76905
• 上游许可： Apache-2.0
• 源码编译： BUILDING.md
• 已知问题： BUILDING.md 中的已知问题
• Python 发布包名： tensorflow-das
• Python 导入名： tensorflow

tensorflow-das 与上游 tensorflow 会安装同名 tensorflow 模块， 请勿在同一虚拟环境或容器中混装。

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Documentation

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Google's Machine Intelligence Research organization to conduct machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.

TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages.

Keep up-to-date with release announcements and security updates by subscribing to announce@tensorflow.org. See all the mailing lists.

Tensorflow ROCm port

Please follow the instructions here to set up your ROCm stack. A docker container: rocm/tensorflow:latest(https://hub.docker.com/r/rocm/tensorflow/) is readily available to be used:

alias drun='sudo docker run \
      -it \
      --network=host \
      --device=/dev/kfd \
      --device=/dev/dri \
      --ipc=host \
      --shm-size 16G \
      --group-add video \
      --cap-add=SYS_PTRACE \
      --security-opt seccomp=unconfined \
      -v $HOME/dockerx:/dockerx'

drun rocm/tensorflow:latest

We maintain tensorflow-rocm whl packages on PyPI here, to install tensorflow-rocm package using pip:

# Install some ROCm dependencies
sudo apt install rocm-libs rccl

# Pip3 install the whl package from PyPI
pip3 install --user tensorflow-rocm --upgrade

For details on Tensorflow ROCm port, please take a look at the ROCm-specific README file.

Install

See the TensorFlow install guide for the pip package, to enable GPU support, use a Docker container, and build from source.

To install the current release, which includes support for CUDA-enabled GPU cards (Ubuntu and Windows):

$ pip install tensorflow

Other devices (DirectX and MacOS-metal) are supported using Device plugins.

A smaller CPU-only package is also available:

$ pip install tensorflow-cpu

To update TensorFlow to the latest version, add --upgrade flag to the above commands.

Nightly binaries are available for testing using the tf-nightly and tf-nightly-cpu packages on PyPi.

Try your first TensorFlow program

$ python

>>> import tensorflow as tf
>>> tf.add(1, 2).numpy()
3
>>> hello = tf.constant('Hello, TensorFlow!')
>>> hello.numpy()
b'Hello, TensorFlow!'

For more examples, see the TensorFlow tutorials.

Contribution guidelines

If you want to contribute to TensorFlow, be sure to review the contribution guidelines. This project adheres to TensorFlow's code of conduct. By participating, you are expected to uphold this code.

We use GitHub issues for tracking requests and bugs, please see TensorFlow Forum for general questions and discussion, and please direct specific questions to Stack Overflow.

The TensorFlow project strives to abide by generally accepted best practices in open-source software development.

Patching guidelines

Follow these steps to patch a specific version of TensorFlow, for example, to apply fixes to bugs or security vulnerabilities:

• Clone the TensorFlow repo and switch to the corresponding branch for your desired TensorFlow version, for example, branch r2.8 for version 2.8.
• Apply (that is, cherry pick) the desired changes and resolve any code conflicts.
• Run TensorFlow tests and ensure they pass.
• Build the TensorFlow pip package from source.

Continuous build status

You can find more community-supported platforms and configurations in the TensorFlow SIG Build community builds table.

Official Builds

Build Type	Status	Artifacts
Linux CPU		PyPI
Linux GPU		PyPI
Linux XLA		TBA
macOS		PyPI
Windows CPU		PyPI
Windows GPU		PyPI
Android		Download
Raspberry Pi 0 and 1		Py3
Raspberry Pi 2 and 3		Py3
Libtensorflow MacOS CPU	Status Temporarily Unavailable	Nightly Binary Official GCS
Libtensorflow Linux CPU	Status Temporarily Unavailable	Nightly Binary Official GCS
Libtensorflow Linux GPU	Status Temporarily Unavailable	Nightly Binary Official GCS
Libtensorflow Windows CPU	Status Temporarily Unavailable	Nightly Binary Official GCS
Libtensorflow Windows GPU	Status Temporarily Unavailable	Nightly Binary Official GCS

Resources

• TensorFlow.org
• TensorFlow Tutorials
• TensorFlow Official Models
• TensorFlow Examples
• TensorFlow Codelabs
• TensorFlow Blog
• Learn ML with TensorFlow
• TensorFlow Twitter
• TensorFlow YouTube
• TensorFlow model optimization roadmap
• TensorFlow White Papers
• TensorBoard Visualization Toolkit
• TensorFlow Code Search

Learn more about the TensorFlow community and how to contribute.

Courses

• Deep Learning with Tensorflow from Edx
• DeepLearning.AI TensorFlow Developer Professional Certificate from Coursera
• TensorFlow: Data and Deployment from Coursera
• Getting Started with TensorFlow 2 from Coursera
• TensorFlow: Advanced Techniques from Coursera
• TensorFlow 2 for Deep Learning Specialization from Coursera
• Intro to TensorFlow for A.I, M.L, and D.L from Coursera
• Machine Learning with TensorFlow on GCP from Coursera
• Intro to TensorFlow for Deep Learning from Udacity
• Introduction to TensorFlow Lite from Udacity

License

This repository is licensed under the Apache License 2.0. See LICENSE. Some files are adapted from tensorflow and other third-party sources; see Third-Party Notices for source and modification notices.

HCU adaptations, modifications, and original contributions by Hygon Information Technology Co., Ltd. are licensed under the Apache License, Version 2.0.

Modified by Hygon Information Technology Co., Ltd.